Learning depth from raw images is a fast evolving area in computer vision community.
The earliest work formulates depth estimation as a mapping function of input single images, constructed by a multi-scale deep neural network [1]} to output the per-pixel depth maps from single images. More accurate depth prediction is achieved by jointly optimizing the depth and self-motion estimation [2]}. These supervised learning methods [1]}, [4]}, [2]} can predict per-pixel depth by training deep neural networks on large data collections of images with corresponding depth labels.
Although they are found outperforming the traditional structure based methods, such as [6]}, their effectiveness are largely reliant on model training and can be difficult to generalize to new scenarios in absence of labeled data.
